The SNePS knowledge representation, reasoning, and acting system has several features that facilitate metacognition in SNePSbased agents. The most prominent is the fact that propositions are represented in SNePS as terms rather than as sentences, so that propositions can occur as arguments of propositions and other expressions without leaving first-order logic. The SNePS acting subsystem is integrated with the SNePS reasoning subsystem in such a way that: there are acts that affect what an agent believes; there are acts that specify knowledge-contingent acts and lack-ofknowledge acts; there are policies that serve as "daemons," triggering acts when certain propositions are believed or wondered about. The GLAIR agent architecture supports metacognition by specifying a location for the source of self-awareness and of a sense of situatedness in the world. Several SNePSbased agents have taken advantage of these facilities to engage in self-awareness and metacognition.

Mixed-initiative systems are a popular approach to building intelligent systems that can collaborate naturally and effectively with people. But true collaborative behavior requires an agent to possess a number of capabilities, including reasoning, communication, planning, execution, and learning. We describe an integrated approach to the design and implementation of a collaborative problem-solving assistant based on a formal theory of joint activity and a declarative representation of tasks. This approach builds on prior work by us and by others on mixed-initiative dialogue and planning systems. We've all had the bad experience of working with someone who had to be told everything he or she needed to do (or worse, we had to do it for them).

Mixed-initiative assistants are agents that interact seamlessly with humans to extend their problem-solving capabilities or provide new capabilities. Developing such agents requires the synergistic integration of many areas of AI, including knowledge representation, problem solving and planning, knowledge acquisition and learning, multiagent systems, discourse theory, and human-computer interaction. This paper introduces seven aspects of mixed-initiative reasoning (task, control, awareness, communication, personalization, architecture, and evaluation) and discusses them in the context of several state-of-the-art mixed-initiative assistants. The goal is to provide a framework for understanding and comparing existing mixed-initiative assistants and for developing general design principles and methods. Mixed initiative assumes an efficient, natural interleaving of contributions by users and automated agents that is determined by their relative knowledge and skills and the problem-solving ...

The Fourth International Conference on Informatics in Control, Automation, and Robotics (ICINCO 2007) was held at the University of Angers from 9 through 12 May 2007. This conference sought to bring together researchers, engineers, and practitioners interested in the application of informatics to control, automation, and robotics, with an emphasis on intelligent systems and various AI technologies, such as expert systems, evolutionary computing, neural networks, and others, in connection to signal processing, systems modeling, and control. Beside the presentation of papers addressing these general topics, several specific themes were discussed during the conference in specialized forums, including special sessions, panels, and workshops, as described in this report. The conference was coorganized by the Institute for Systems and Technologies of Information, Control, and Communication (INSTICC) and the University of Angers, through the Laboratoire d'Ingénierie des Systèmes AI Magazine Volume 28 Number 4 (2007) ( AAAI) The conference was also held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The ICINCO 2007 conference program included oral presentations (full papers and short papers), as well as posters, organized in three simultaneous tracks: "Intelligent Control Systems and Optimization," "Robotics and Automation," and "Systems Modeling, Signal Processing, and Control."

I consider how to represent and reason with users' preferences. While areas of economics like social choice and game theory have traditionally considered such topics, I will argue that computer science and artificial intelligence bring some fresh perspectives to the study of representing and reasoning with preferences. For instance, I consider how we can elicit preferences efficiently and effectively. With one agent, the agent's desired goal may not be feasible. The agent wants a cheap, low-mileage Ferrari, but no such car exists.

Extempo Systems, Inc., was founded in 1995 to commercialize intelligent characters. Our team built innovative software and novel applications for several markets. We had some early-adopting customers during the Internet boom, but the company could not survive the significant downturn in corporate IT spending when the bubble burst. In 2004, Extempo ceased operations and was formally liquidated. Although our commercial venture failed, we advanced the technology for intelligent characters and learned a lot about how (not) to take them to market.

In many cooperative multiagent domains, the effect of local interactions between agents can be compactly represented as a network structure. Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. A distributed constraint optimization problem (DCOP) is a useful framework to reason about such networks of agents. Given agents' inability to communicate and collaborate in large groups in such networks, we focus on an approach called k-optimality for solving DCOPs. In this approach, agents form groups of one or more agents until no group of k or fewer agents can possibly improve the DCOP solution; we define this type of local optimum, and any algorithm guaranteed to reach such a local optimum, as k-optimal.

In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multiattribute) structure. We give an introduction to preference handling in combinatorial do - mains in the context of collective decision making and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, which is known as computational social choice and which brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

Interactive artificial intelligence systems employ preferences in both their reasoning and their interaction with the user. This survey considers preference handling in applications such as recommender systems, personal assistant agents, and personalized user interfaces. We survey the major questions and approaches, present illustrative examples, and give an outlook on potential benefits and challenges. A popular example is Netflix, which gives customized movie recommendations to each user, using past movie ratings of the user and his or her friends. Netflix and other preference-aware interactive systems share the common aim of aiding the user in carrying out tasks--from finding a product to editing a document--by eliciting preferences from the user, inferring a preference model, and using the model to decide when and how to take action.

Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or mon itoring; for example, they can plan an attack avoiding existing pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization must provide distinct weights to dif ferent actions based on their complex costs and benefits. To this end, this article describes a promising transition of the lat est in multiagent algorithms into a deployed application. In particular, it describes a software assistant agent called AR MOR (assistant for randomized monitoring over routes) that casts this patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features.